Classification and challenges of non-functional requirements in ML-enabled systems: A systematic literature review

Abstract

Machine learning (ML) is nowadays so pervasive and diffused that virtually no application can avoid its use. Nonetheless, its enormous potential is often tempered by the need to manage non-functional requirements (NFRs) and navigate pressing, contrasting trade-offs. In this respect, we notice a lack of systematic synthesis of challenges explicitly tied to achieving and managing non-functional requirements (NFRs) in ML-enabled systems. Such a synthesis may not only provide a comprehensive summary of the state of the art but also drive further research on the analysis, management, and optimization of NFRS of ML-enabled systems. In this paper, we propose a systematic literature review targeting two key aspects such as (1) the classification of the NFRs investigated so far, and (2) the challenges associated with achieving and managing NFRs in ML-enabled systems during model development Through the combination of well-established guidelines for conducting systematic literature reviews and additional search criteria, we survey a total amount of 130 research articles. Our findings report that current research identified 31 different NFRs, which can be grouped into six main classes. We also compiled a catalog of 26 software engineering challenges, emphasizing the need for further research to systematically address, prioritize, and balance NFRs in ML-enabled systems. We conclude our work by distilling implications and a future outlook on the topic.

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